View source: R/gcn_inference.R
exp2gcn_blockwise | R Documentation |
Infer gene coexpression network from gene expression in a blockwise manner
exp2gcn_blockwise(
exp,
net_type = "signed",
module_merging_threshold = 0.8,
SFTpower = NULL,
cor_method = "pearson",
TOM_type = NULL,
max_block_size = 5000,
min_module_size = 30,
...
)
exp |
Either a 'SummarizedExperiment' object, or a gene expression matrix/data frame with genes in row names and samples in column names. |
net_type |
Character indicating the type of network to infer. One of 'signed', 'signed hybrid' or 'unsigned'. Default: 'signed'. |
module_merging_threshold |
Numeric indicating the minimum correlation threshold to merge similar modules into a single one. Default: 0.8. |
SFTpower |
Numeric scalar indicating the value of the |
cor_method |
Character with correlation method to use. One of "pearson" or "biweight". Default: "pearson". |
TOM_type |
Character specifying the method to use to calculate a topological overlap matrix (TOM). If NULL, TOM type will be automatically inferred from network type specified in net_type. Default: NULL. |
max_block_size |
Numeric indicating the maximum block size for module detection. |
min_module_size |
Numeric indicating the minimum module size. Default: 30. |
... |
Additional arguments to |
A list containing the following elements:
MEs Data frame of module eigengenes, with samples in rows, and module eigengenes in columns.
genes_and_modules Data frame with columns 'Genes' (character) and 'Modules' (character) indicating the genes and the modules to which they belong.
params List with network inference parameters passed as input.
dendro_plot_objects List with objects to plot the dendrogram
in plot_dendro_and_colors
. Elements are named 'tree' (an hclust
object with gene dendrogram), 'Unmerged' (character with per-gene module
assignments before merging similar modules), and 'Merged' (character
with per-gene module assignments after merging similar modules).
Fabricio Almeida-Silva
data(filt.se)
# The SFT fit was previously calculated and the optimal power was 16
cor <- WGCNA::cor
gcn <- exp2gcn_blockwise(
exp = filt.se, SFTpower = 18, cor_method = "pearson"
)
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